In the above equations,
\(\lambda _{1}\)  to \(\lambda _{4}\)  are hyperparamters that are tuned during training.
We use the \(L_{2}\)  function as the adversarial loss for our GAN, similar to [1]}.
We also use the \(L_{1}\)  function for the identity loss, which helps reduce texture and color shift between the generated image and the ground truth.
We considered adding other loss functions, such as the perceptual loss [2]} and SSIM loss [3]}, but they did not improve our results.
